An active shape model has been used for localization of shapes in images. The active shape model represents the shape by use of a linear statistical model. The object shape is typically represented by N points, and the model is learned from manually annotated data. The number of points representing the model is fixed during a training phase. The number of points remains the same while fitting the shape to an unseen image. Each point of the model can be assigned with a weight. This weight is either entered manually or automatically. However, the number of points remains unchanged. In an active shape model, more points increase the shape fitting performance in an unseen image. On the other hand, a large number of points also accounts for high computational costs.
Biometric identification has been done using the ocular region of faces. Information captured around the ocular region is complementary to iris when the iris acquisition is unconstrained and iris quality is low such as blurred, obscure or gazed. The ocular region contains rich skin micro-pattern texture information, such as pores, spots, wrinkles, flat areas, etc. This information helps to distinguish subjects among themselves. Non skin patterns (eyebrow, tear duct, eye fold), if available, are also relevant. The feature extraction methods (e.g. local binary pattern, histogram of oriented gradients, Gabor wavelets) used for biometry identification are typically used for one fixed resolution. The parameter setting for these methods is optimized for a given image resolution. The various features are fused and form a representation for a given ocular image.